SHB: Collaborative Research:Context-aware, dynamic, smart checklists: key cyber-infrastructure for systems delivering quality health care

该项目将开发和评估一个原型系统，为卫生保健过程的执行者提供在线过程监测和指导。美国医学研究所的一份报告估计，美国医院每年有近10万人死于可预防的差错，随后的报告表明，其中许多差错是由于医院流程的复杂性造成的。该项目将演示如何使用上下文信息(追溯的、当前的和预期的)为流程执行人员提供及时的信息，以减少错误，提供即将到来的危险的快速警告，并改善结果。将开发积累和表示历史数据的技术，并将其提供给基于配置文件的分析技术，该技术将评估概率并支持进行细粒度的过程区分。这些能力将为基于证据的持续流程改进提供坚实的技术基础。项目技术将首先使用由流程模型驱动的模拟生成的合成事件流进行评估，然后通过使用患者人体模型对护生进行人工模拟，最后与模拟临床环境中的医疗专业人员进行评估。检查过程包括输血、化疗、用药和患者身份验证。社区在医院急诊科应用医疗检查表的早期成功和最近的实验结果以及积极的流程指导都是积极的迹象，表明这里提出的方法有很好的机会获得接受并改善医疗结果。此外，尽管这里开发的技术是专门针对医疗保健进行评估的，但也将适用于越来越多地应用于社会广泛领域的人力密集型系统。经过验证的医疗流程和拟议的原型将成为以当前最佳做法教育医疗专业人员的有效框架。该项目还将教育计算机科学和工程专业的学生了解医学领域带来的挑战，鼓励这一新兴跨学科领域的新一代专家。它的社会吸引力还将有助于吸引聪明、精力充沛的少数群体和女学生，从而有助于扩大代表不足的群体在STEM，特别是在计算机科学和工程领域的参与。

This project will develop and evaluate a prototype system to provide on-line process monitoring and guidance to the performers of health care processes. An Institute of Medicine report has estimated that nearly 100,000 people per year die in US hospitals from preventable errors, and subsequent reports have suggested that many of these errors arise due to the complexity of hospital processes. The project will demonstrate how contextual information (retrospective, current, and prospective) can be used to provide process performers with timely information that could reduce errors, provide expedited warnings of impending hazards, and improve outcomes. Techniques to accumulate and represent historical data will be developed and will feed into profile-based analysis techniques that will evaluate probabilities and support making fine-grained process distinctions. These capabilities will provide a strong technological foundation for evidence-based, continuous process improvement.Project technologies will be evaluated first using synthetic event streams generated by process model driven simulations, then by human simulations with nursing students using patient mannequins, and finally with medical professionals in simulated clinical settings. Processes to be examined include blood transfusion, chemotherapy, medication administration, and patient identification verification. Early community success in applying medical checklists and recent experimental results with proactive process guidance in a hospital emergency department are positive indications that the approaches proposed here have an excellent chance of gaining acceptance and improving medical outcomes. Moreover, the technologies developed here, although evaluated specifically for health care, will also apply to human-intensive systems increasingly employed in a wide range of domains in society. The validated medical processes and the proposed prototype will be an effective framework for educating medical professionals in current best practices. This project will also educate computer science and engineering students about the challenges posed by the medical domain, encouraging a new generation of specialists in this emerging interdisciplinary field. Its societal appeal will also help engage bright, energetic minority and female students thereby helping broaden the participation of underrepresented groups in STEM generally and in computer science and engineering particularly.